Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using average MPG and another category that you feel may influence fuel efficiency. Answer the following questions.
Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot?
Answer: The heavier a car is, the more energy (gas) it requires to move.
Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Weight is the x-axis, mpg is the y-axis. Just as y depends on x, mileage will depend on weight.
Do you believe there is a relationship between the two categories? Why or why not?
Answer: Yes because of basic laws of physics, more mass requires more energy to achieve a specific velocity.
If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Answer: Negative slope because as weight increases mileage decreases; heavier cars get less mpg.
Create the linear regession equation in Excel, which Excel calls the trend line. Click the boxes to create both the equation and the r2 value on the graph. Answer the following questions.
What is your regression equation? Explain what the equation means in words.
Answer: y = -91.695x + 5432; As weight (x) increases by 1 lb, mileage decreases by about 92, but begins at 5432.
What is your r2 value? Is this a strong correlation? Why or Why not? If you are not sure, try searching the internet for supporting documents. Provide URL's for where you find your information
Answer: 0.704; strong correlation would be closer to 1.0
Based on all the information you have, has your belief about the relationship of the two categories changes? Why or why not?
Answer: no because the general idea is that the mileage decreases with increasing weight and that has proven true
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
How would you determine which equation had the best relationship?
Answer: Whichever line passed through the most points.
Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Answer: No it didn't pass through as many points as the logarithmic function. The log function passed through the most points.
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.
cars group5.xls
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Back to Activity1Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using average MPG and another category that you feel may influence fuel efficiency. Answer the following questions.
Answer: The heavier a car is, the more energy (gas) it requires to move.Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot?
Answer: Weight is the x-axis, mpg is the y-axis. Just as y depends on x, mileage will depend on weight.Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Yes because of basic laws of physics, more mass requires more energy to achieve a specific velocity.Do you believe there is a relationship between the two categories? Why or why not?
Answer: Negative slope because as weight increases mileage decreases; heavier cars get less mpg.If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Regression Questions
(What is Regression?)
Create the linear regession equation in Excel, which Excel calls the trend line. Click the boxes to create both the equation and the r2 value on the graph. Answer the following questions.
Answer: y = -91.695x + 5432; As weight (x) increases by 1 lb, mileage decreases by about 92, but begins at 5432.What is your regression equation? Explain what the equation means in words.
Answer: 0.704; strong correlation would be closer to 1.0What is your r2 value? Is this a strong correlation? Why or Why not? If you are not sure, try searching the internet for supporting documents. Provide URL's for where you find your information
Answer: no because the general idea is that the mileage decreases with increasing weight and that has proven trueBased on all the information you have, has your belief about the relationship of the two categories changes? Why or why not?
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
Answer: Whichever line passed through the most points.How would you determine which equation had the best relationship?
Answer: No it didn't pass through as many points as the logarithmic function. The log function passed through the most points.Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.